Please, I need to know the best meta-heuristic and hybrid meta-heuristic algorithms (except for Genetic Algorithm) for finding the optimal or near optimal solution of stochastic scheduling problems.
Dear Neelam,
In fact, there is no better heuristic than another, For exemple betwen GA and PSO, PSO is more simple in terms of complexity, also in time of compilation, it converges rapidly, except that in some conplex problems GA shows more relevant results, therefore the best heuristic depends on the problem in question, and how it complies with the criterion of heuristique (parameter setting).
Greeting
Agree with previous comments. Good parametrization of algorithms is indeed a bottleneck. I can recommend you the "cross-entropy" method, please see:
G. Beruvides, R. Quiza, and R. E. Haber, "Multi-objective optimization based on an improved cross-entropy method. A case study of a micro-scale manufacturing process," Information Sciences, vol. 334, pp. 161-173, 2016.
No metaheuristic is better than all the rest to solve all the problems. So, the best metaheuristic does not exist. What exists are differences between metaheuristics when applied to a particular problem (such as continuous optimization, boolean satisfiability, vehicle routing, etc). However, "the average performance of any pair of metaheuristic algorithms across all possible problems is identical." (Wolpert and Macready, 1997). I recommend You to read the linked paper to get a better understanding of this topic.
https://ti.arc.nasa.gov/m/profile/dhw/papers/78.pdf
Actually, it is always problem specific, a few metaheuristics are good for few problems rest are for other problems, Recently many researchers are performing modifications i am also experimenting with, crossover operators, elitism, control operators but the choice of the parameters is always a bottleneck as it is said earlier.
Particle Swarm Optimization, Simulated Annealing, Artificial Bee Colony
There are many algorithms, which all better than GA. I list all below:
PSO : Particle swarm optimization
ACO : Ant colony optimization
BA : Bees Algorithm
ABC : Artificial Bees Algorithm
As in previous comments, there is no best meta-heuristic algorithm, it simply depends on the objective function you are trying to optimise.
Examples include:
Optical inspired Optimization (OIO).
Forest Optimization Algorithm (FOA)
Tree - Seed Algorithm (TSA)
its question is wrong. because we haven't answer for that, you should try some algorithms on your problem and find best for that, for example bets algorithm for TSP is ACO. but ACO is very slow and for solve TSP usually uses GA or another district metaheuristic. so you can try some metaheuristic and compare results.
Dear Mahmoud question is right that's why you have given the answer of this question.
It is true that it depends a lot on the type of problem. I have used with very satisfactory results a hybrid metaheuristic based on GA(EA), DE and PSO.
All metaheuristic optimization approaches are alike on average in terms of their performance. The extensive research studies in this field show that an algorithm may be the topmost choice for some norms of problems, but at the same, it may become to be the inferior selection for other types of problems. On the other hand, since most real-world optimization problems have different needs and requirements that vary from industry to industry, there is no universal algorithm or approach that can be applied to every circumstance, and, therefore, it becomes a challenge to pick up the right algorithm that sufficiently suits these essentials
For more information on this subject, refer to page 40 of the following article:
Article Sea Lion Optimization Algorithm for Solving the Maximum Flow Problem
More the recently developed one, the higher the accuracy is, check CPA and RSA
There is no specific ansawer about this question, because every modern metaheuristic i have read about seems to achive very good performance, and modern algorithms are slightly deffirent from each other duo the term of performance, yet i advise you to look for modern and simple metaheuristic ones (simple yet efficient like crow search algorithm, Jaya algorithm, ...etc.).
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